Manuscript Outline
Cram
Title:
Abstract
Models and observations suggest that that particle flux attenuation is lower across the mesopelagic zone of anoxic environments compared to oxic ones. This attenuation is likely a function of microbial metabolism, as well as agregation and disaggregation by zooplankton. Analysis of particle size spectra provide insight into the relative roles of aggregation, disaggregation and remineralization.
We measured particle size profiles at a station in the core of the Eastern Tropical North Pacific Oxygen Minimum Zone (ETNP OMZ) using an underwater vision profiler (UVP) multiple times of day, at different times of day, over the course of a week. We normalized our UVP measurements by comparing them to particle flux measurements measured by sediment traps, and we compared our observations to UVP measurements from a site at a similar latitudes but with non limiting oxygen concentrations.
Particle flux attenuated more slowly in the ETNP OMZ than in a similar latitude oxic environment transect. Indeed, flux appeared to increase not only in the photic zone, but at several depth intervals within the anoxic zone, as well as at the base of the OMZ.
By comparing observed particle size distributions to those predicted by models in which particles remineralized, but did not aggregate or disaggreagate, we were able to quantify net rates of disaggreagation and how they varied throughout the water column. We found that particles appeared to disaggregate above and below the oxygen minimum zone, but not in the core of the OMZ.
These patterns suggest that the oxygen minimum zone affects, but does not eliminate the role of zooplankton in transporting organic carbon to depth, and breaking large particles into smaller particles due to sloppy feeding.
Introduction
And:
- Some steinberg model of the role of zooplankton in flux.
Likely different in OMZ
Flux attenuates less in oxygen minum zones (Cram) and models have suggested a role of suppressed remineralization In the ETNP OMZ decreased flux attenuation has been observed near the coast, but not in the open ocean.
Stuff that is known about zooplankton particle interactions.
Stuff that is known about particle size distributions (esp, Guidi and Kiko)
Particle size distributions can be compared to flux by normalizing to trap data (Guidi).
In the ETNA, there is a weaker OMZ and particles were compared to acoustic profiles of organims. {Findings} (Kiko 2).
But
- The temporal dynamics of particle size distribution have not been examined in the open ocean or oxygen minimun zone at this time scale.
- Particle size has not been normalized to observed flux in this region.
- It is not known how migratory zooplankton affect particle size distributions, and if they lead to diel variability in particle flux that might affect overall transport into the deep ocean.
- It is not fully known how oxygen limitation affects particle size dynamics and flux.
- Nobody has quantified non remineralization like processes.
Therefore
We conducted the first ever time-series analysis of particles, in an oxygne minimum zone. With the goal of normalizing to observed flux, and comparing particle size data to acoustic data about the distributions of zooplankton and larger orgasnisms.
We compare this data to acoustic signature of zooplankton.
We modified the a particle remineralization and sinking model (PRiSM) (Devries, Cram) to estemate particle size distribution changes that would occur under dynamics where particles only sink and remineralize, and compared observed distributions to ones expeced by the model. This allowed us to quantify the magnitude of non-PRiSM like processes
Scientific question:
- How do the particle size distribution at one location in the oligotrophic Eastern Tropical North Pacific evolve with respect to depth, and how does it vary over time?
We hypothesized
- Temporal day to day variability in paticle number, particles size distribution slope and flux would be evident.
- This variability would relate to the location of migratory zooplankton, with a combenation of increased particle flux and disaggregation present where zooplankton occur.
- Disaggregation and particle production by zooplankton might lead to particle size patterns that cannot be explained by remineralization and sinking alone.
Methods
Most measurments were taken on board the R/V Sekuliaq from 07 January 2017 thorugh 13 January 2017 at XXX Lat XXX Long. Data are compared against {describe station 100 here}
Water property measurment
We measured water properties of temperature, salinity, fluorescence, oxygen concentration and turbidity using a XXX CTD {get sensor information}. Data were processed using seabird software and analyzed and visualized in R.
Particle size measurments
Particle size data were collected by underwater vision profiler (UVP) that was mounted to the CTD and deployed for all CTD casts shallower than 2500m. A UVP is a combenation camera and light source that describes the abundance and size of particles from 100 microns to several centemeters in size (ref). Particles have been previously shown to be primarely “marine snow” but may also include a small number of zooplankton and visual artifacts. UVP data were processed using custom matlab scripts, uploaded to XXX, and analyzed in R.
Flux measurments
Particles were collected in incubating particle traps (REF). Traps were used to performincubation studies which will be reported elsewhere. As part of these studies, the traps also generated data about carbon flux, which is reportd here. Two types of traps were deployed. The particles were collected in two kinds of traps. One set of traps, generally deployed in shallower water had a solid cone opening with a X m diameter cone opening. The second set had larger conical Xm diameter 200 micron nylon mesh net at the top. In all cases particles collected in the net or cone fell into one of two chambers. The “plus-particles” chamber collected particles from the net and incubated them for an amount of time that ranged from X days to X days. The top-collector trap collected particles, and then returned immediately to the surface. We prferentially used data from the “top-collector”; however in many cases, data was only available forom the “plus-particles” trap, in which case we used that data.
{What if I move the analysis to the results section} ## Analysis
The total number of particles in each size were visualized. We calcualted total particle number by multiplying over all size bins.
Particle size distribution
We determined the slope and intercept of the particle size distribution spectrum by fitting a power law of form \(log(# Particles) = Int + PSD * log(Size)\). Because large particles were infrequently detected we used a poisson- general linear model that considered the volume of paricles sampled and that assumed that the residuals of the data followed a poisson (rather than normal) distribution.
Estimating particle flux
We estemated particle flux by assuming that particle flux in each size bin followed the equation flux = C_f * d ^ a (guidi 2008 ref). We used Alledredge and XXX’s estemate of a and then fit the observed flux measurments to particle measurments to estemate C_f. We then used this equation to estimate flux from the data collected by the UVP.
Size specific information
We seperately analyzed total particle numbers, particle size distribution, and particle flux for particles larger than 53 microns, and those smaller than 53 microns to determine the relative contributions of these two particle classes to particle properties
Variability
We quantified whether the sample to sample variability
Model Formuation
Results
Chemical Data
Temperature, salinity, density, fluorescence, oxygen and turbidity were examined (Figure 2). The oxygen minimum zone extends from the base of the primary chlorophyl maximum layer (XXXm) to 950m. This site, like many in the ETNP OMZ has two fluorescence maxima, one above, and one inside of the OMZ. Turbidity is highest above the omz, and decreases with depth, and then increases again at the base of the oxygen minimum zone.
Acoustic data
EK60 data, figure 8. show a peak of migratory plankton which travel from the surface at dawn to a maximum depth of XXXm and then return to the surface at dusk.
- Similar patterns between different freuencies, with better resoultion by the lower bandwidth. While we expect that small organisms likely have the greatist impact on particles,
Flux data
Flux measurmenets at station P2 were consistant between the different particle trap types and chambers measured, and showed a profile that broadly represented a power law with respect to depth, with the exception that flux appeared to increase around 500m.
Particle abundance measurments
Particles at all depths are most abundant in the smallest size fraction and less abundant in larger size fractions. At every depth, partiles roughly followed a power law with respect to size. Thus, a particle size distribution slope could be calculated at each depth and compared. {This is what the PSD slope does}.
These patterns differed from those seen at the P16 transect, station 100 where XXXXX.
Estemated particle flux
When fit to the flux profile data (per Guidi et al.) we predicted that the particle flux to size ratio was best goverend by the relationship flux = cXX * size ^ aXX. As the exponent parameter in this best fit was much smaller than predicted values, we also performed a fit where that value was taken from the literature and set to aXX1. In this case the relationship was governed by the equation flux = 10.51 * size ^ axx1. This resulted in a flux profile that broadly fit the results seen by the traps (figure XX).
Large vs small particles
Generally the profiles are related. Most flux appears to be from smaller particles.
Possible particle transport by zooplankton
There was an apparent increase in particle flux between XXX and XXX m (Figure X), which corresponded to the region where migratory organsisms spent most of the day (Figure X). To estemate how this flux increased transfer efficiency, we compared the sequestration flux (flux through the depth bin centered at 975m) to an estemated sequestration flux if a power law, typical of the flux attenuation below 500m, was applied to the flux out of the photic zone (185.5m bin).
Extrapolating the power law relationship seen below 500m to the flux at the base of the photic zone suggests that if it was not for zooplnkton transport the transfer efficiency would be 1/3 of what it is observed to be.
{Compare to P2.}
Variations from remineralization and sinking dynamics
By comparing the particle size distributions at each depth to the particle size distribution that woudl be predicted from the particle size distribution one depth bin shallower in the water column, and the observed flux attenuation, we were able to examine how particle number attenuation differed from what would be expected if particles sank and remineralized following PRiSM model like dynamics. We observed that there were more small particles than would be predicted from remineralization dynamics only. By comparing the observed flux of particles < 53 microns to the reminineralization-predicted flux of small particles, we observed that above 500m, there was excess observed small particle flux above 500m (Figure X).
{Discussion: This suggested that one of two things could be happening. The first is that particle disaggregation, possibly caused by sloppy feeding by zooplankton, could be breaking large particles into smaller particles. The other possibility is that zooplankton could be preferentially removing larger particles via grazing, leaving smaller particles behind}.
Figures
Figure 1. Map of the ETNP Oxygen Minimum Zone and the location of station P2. Colors indicate chlorophyll concentrations at the surface, while the red ouline signifies the region containing low oxyegen.
Figure 3. Mean oceanographic parameters at ETNP station P2 (A) temperature (B) Salinity, (C) Chlophyll Concentrations (D) Oxygen concentrations (E) Turbidity [Add Figure labels, possibly merge into fewer panels in base R].
Figure 2. An example of observed particle size distribution spectra. These are depth binned data from between X and X m deep in the water column from the cast that occurred at DATETIME for stn_043. A total volume of XXX L of water are sampled herein. Points indicate (A) total numbers of observed particles and (B) particle numbers normalized to volume sampled and particle size bin width. The line indicates the predicted best fit line of the data. The line was fit on the bin and volume normalized data by a negative-binomial general linear model. The line in panel A indicates predictions from this same model, rescaled into absolute particle space.
Figure 4. (A) Observed, volume normalized total particle numbers from 9 casts taken at different times of the day at ETNP station P2. (B) Calculated particle size distribution slopes of those particles. These data have not been binnedby depth.
Figure 5. As above, but for the final cast taken at ETNP station P2 and the only cast collected from the P16 transect at the station 100. P16 Station 100 was chosen because it is at a similar latitude to ETNP station P2. (A) Total particle numbers, (B) Particle size distribution.
Figure 6. Particle flux, measured from sinking traps large symbols. Data from the “plus particles” and “top collector” samples from both cone and net traps were collated to generate these data. Trap types are shown by the shape and color of the large points. Superimposed are binned estemates of particle flux generated by fitting the sum of particle numbers all four profiles, binned as in Figure X, to the trap observed flux. The four points enclosed by the rectangle are unusually low compared to other traps collected at the same depth, and were therefore excluded from the fit.
Figure 7. An exploration of model predicted particle flux at ETNP station P2. All profiles are depth binned with higher resolution towards the surface (methods). (A) Flux profiles in the top 1000m of the water column. (B) A more detailed depiction of the area enclosed by the rectangle in A. (C) The rate in change of flux, devided by the rate in change in depth. We show the fifth root of these values in order to focus on values that are close to zero, and to show that flux increases at some depths, at some time points.
Figure 8. [Move to supplement – this is confusing] Depth binned particle number (volume normalized), particle size slope (psd), and flux (estimated as in Fig. 4) for large (\(>= 500\, \mu m\)), small (\(< 500 \, \mu m\)) and total particles, at both stations. 
Figure 9. Quantification of non remineralization and sinking like processes. Points indicate the difference between the observed small particle flux, and the flux that would be estimated if particles from the size distribution in the depth bin above remineralized and sank only following the PRiSM model. Values are normalized to the change in depth. Thus values are uMol Carbon/m3/day
Figure 10. Acoustic data, measured by EK60, measured over the course of the experiment. Shown are data from the 18000 Hz frequency band, which have highest depth penetration, but which appear to co-occur with data from other frequency bands (see Figure SX). Values are in return signal intensity and have not been normalized to observed biomass.
#mOVED Maybe skip? {done, notadded} Figure S1. A profile of data generated by the UVP. At each depth the abundance of particles at each size are color coded on a log scale. Particle sizes where no particles in that sample were seen are represented with a smaller black dot.
{done not added} Figure S2. Comparason of total particle number and particle size distributions of all casts taken at the ETNP station P2. Points indicate individual samples, while ribbons indicate confedence intervals of those sampeles. The overlapping confedence intervals suggest that there is not a statistically detectable difference between the different casts at the same station. {I’d like a more quantitative metric}.
Figure S3. Acoustic data, measured by EK60, measured over the course of the experiment. Shown are data from the all frequency bands. Values are in return signal intensity and have not been normalized to observed biomass.